Data Science in Marketing: Here's How it's Rewriting the Rule Book

Advances in technology mean brands are able to collect more marketing data than ever from their customers and at every stage of their journey. But this information won’t fulfill its potential unless it’s transformed into actionable insights, which is where data science comes in.

Unleashing techniques including machine learning, clustering and regression will transform your marketing and demonstrate the power of data science. This enables you to incorporate the full funnel and generate unprecedented insights at scale.

Data science marketing trends

While it’s becoming universally known among marketers that they can’t ignore data science, the way it’s applied is varied. The reality is that the area is constantly evolving and new applications are emerging all the time. Among the data science trends marketing departments should be aware of are:

1. Advertising data science

The digital realm offers universal access to advertising in a way that costly traditional platforms like television never did. In order to harness the transformative power of digital advertising, brands must take a data-driven approach to ensure it’s effective and offers good returns on investment.

2. Segmenting and targeting

Segmenting your audience in order to target them with highly-specialized offerings is more possible than ever before due to the marriage of marketing and data science. Taking semi-structured and unstructured data and analyzing it to inform decisions is key to delivering what audiences want.

3. Increased speed and performance

Analytics-led marketing means that key processes within a campaign, from planning to execution, can be achieved more quickly and with better results. Not only that, but measuring the effectiveness of marketing is easier with analytics rolled out from beginning to end and used to inform decisions going forward.

4. Real-time testing

There’s no longer a need to wait until the end of a campaign or months into the implementation of a marketing strategy to see if it’s effective. Real-time experimentation means there’s less speculation and decisions can be made immediately to ensure maximum engagement is achieved consistently.

Utilizing analytics for customer segmentation

Customer segmentation is among the most game-changing applications for data science and enables marketers to slice the data and group customers like never before. There are three main approaches to segmentation:

• Touchpoint engagement segmentation
• Purchase pattern segmentation
• Microsegmentation

They all rely on analysis of data to define groups of customers so that marketing can be more personalized based on their preferences.

Real-time analytics

Setting up real-time algorithms to analyze both customer data and operational data increases revenue from marketing campaigns. By utilizing information gathered from social media platforms and communication technologies, brands can gain actionable insights on the preferences, desires and needs of their customer base.

Marketers can expect to obtain the following from real-time analytics:

• Detailed information about customers
• An indication as to which platforms are the most efficient
• Insight into providing better customer experience
• Perform real-time testing
• Identify the best ways of working
• React and respond to situations immediately

Predictive analytics

Despite the importance of real-time analytics, marketers’ use of algorithms shouldn’t stop there. Forecasting the future with high probability is another way that data science is taking the risk out of engagement efforts. Putting predictive analytics into action can lead to:

• Highlighting correlations in customer behavior to understand future purchasing tendencies
• The ability to qualify and prioritize leads
• Data visualization to determine which products or services to bring to market
• Identifying the highest value customers so they can be successfully targeted

Sentiment analytics

Another useful set of analytics to track is sentiment, which refers to the initial reaction customers have to your brand when they find your website or social media channels. Often shaped by the reviews or responses of others, sentiment analytics are a good measure of your online reputation.

Utilize machine learning algorithms to process this data quickly and efficiently to produce useful insights that help you tap into the emotions of potential customers for better results. Assigning values to individual words - negative, neutral or positive - will help you to give each interaction a score and identify which areas are performing well and those that need improvement.

Boosting customer loyalty

Data science shouldn’t just be applied when dealing with new customers, but can also be a valuable technique for improving customer loyalty. Identifying personas, trends and motivations in your established customer base can help you better serve them and predict which products or services they may require next. As they’ve already purchased from you in the past, there’s a level of trust that’s already been established and is worth nurturing.